genetic algorithm application for traffic light control matlab source code
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Hi am arya i would like to get details on genetic algorithm application for traffic light control matlab source code...i need help
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The monitoring and control of vehicular traffic and
pedestrians pose a major challenge to transport authorities
around the world. The escalating number of vehicles in
cities not only has a huge environmental impact, but also
results in loss of lives on the road. This situation demands
a comprehensive approach involving a system in which
both the traffic controls for vehicles and pedestrians are
coordinated so that road users are safe and traffic is
smooth flowing.
Currently, pedestrian crossings pose a significant hazard in
many countries, both in developed and developing
countries due to the increase in vehicles number. Each
year a staggering figure of 500,000 pedestrians are killed
all over the world and in China alone from 2000-2004, half
a million pedestrians were killed.
The European Transport Safety Council (ETSC) claims
that 15 to 30 percent of the transportation mode used is
walking. According to a telephone survey conducted by
the Royal Automobile Club of Spain in the year 2000,
walking is highly recommended as part of a healthy
lifestyle with no negative side effects. However, it has
been the victim of badly controlled traffic, thus increasing
the mortality rates of road users. In the large cities of
Europe, especially in Spain, people walked to their
destinations but this is being seen as dangerous as
pedestrians are more vulnerable to road accidents than
passengers and drivers of cars .
In a conventional traffic light controller, the traffic lights
change at constant cycle times which is clearly not the
optimal solution. The system calculates the cycle time
based on average traffic load and disregards the dynamic
nature of the traffic load, which aggravates the problem of
congestion. Consequently, we see an urgent need to
optimize traffic control algorithms to accommodate the
increase in vehicles in urban traffic that experience long
travel times due to inefficient traffic light controls and to
improve pedestrian’s safety.
In this paper, we propose an optimal control of traffic
lights using genetic algorithm (GA), in a four-way, twolane
junction with a pedestrian crossing. The innovative
design of the pedestrian crossing is also based on such
algorithm, which includes pedestrians as one of the
parameters. Genetic algorithm is introduced in the traffic
control system to provide an intelligent green interval
response based on dynamic traffic load inputs, thereby overcoming the inefficiencies of conventional traffic
controllers. In this way, the challenges are resolved as the
number of vehicles are read from sensors put at every lane
in a four-way, two-lane junction and pedestrianss are
monitored at the road junction.
The features inherent in genetic algorithm play a critical
role in making them the best choice for practical
applications, namely optimization, computer aided design,
scheduling, economics and game theory. It is also selected
because it does not require the presence of supervisor or
observer.
However, genetic algorithm, without prior training,
continuously allow permanent renewal of decisions in
generating solutions. Instead of trying to optimize a single
solution, they work with a population of candidate
solutions that are encoded as chromosomes. Within these
chromosomes are separate genes that represent the
independent variables for the problem at hand.
There are a number of specific attributes of genetic
algorithms that give them an edge over other traditional
optimization techniques. These are:
• A genetic algorithm works from a population, not a
single point, and hence it is less likely to be trapped
at a local optimum.
• Derivative freeness, i.e. a genetic algorithm does not
need the objective function’s derivative to do its
work.
• Flexibility, i.e., a genetic algorithm can function just
fine regardless of how complex the objective
function is; the only thing it requires of the function
is that it is executable (i.e., its value can be calculated
given the values of the decision variables).
• Because of its implicit parallelism, a genetic
algorithm can handle combinatorial problems
efficiently. It has been shown that as the size of the
search space or number of solutions increases
exponentially, the time requirements for the GA to
reach a solutions only grow linearly. This feature is
particularly useful for on-line optimization of
transportation problems such as traffic control.
• A genetic algorithm naturally lends itself to parallel
implementation. This follows from its functional
components structure.
• Genetic algorithm, is, for the most part, based on
intuitive notions and concepts.
Our preliminary review of the literature indicates that
genetic algorithm has not been tested on pedestrian
crossings. We have, therefore, attempted to implement this
algorithm and study its effects on this problem.
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